LLMs · Multi-Agent Systems · RAG · APIs · Distributed Systems · Cloud Infrastructure
I'm Rahul Thakur, an AI Systems Architect & Backend Engineer with 7+ years of experience building software systems that need to be reliable beyond the demo.
My current focus is at the intersection of:
Artificial Intelligence
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LLM Systems AI Agents
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Production Backend
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APIs Infra
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Cloud at Scale
I care about the part that happens after the prototype works:
- How does the system handle 10,000 users?
- How do agents recover when tools fail?
- How do we control LLM cost and latency?
- How do we evaluate AI quality?
- How do we secure enterprise data?
- How do we observe, debug and improve AI behavior?
- How do we turn an AI experiment into a maintainable platform?
That's the engineering problem I enjoy solving.
AI is easy to demo. Reliable AI is an engineering discipline.
I don't optimize only for impressive outputs.
I optimize for:
Reliability → systems that don't randomly fall apart
Scalability → architecture that grows with demand
Observability → know what the AI is actually doing
Security → enterprise data stays protected
Performance → latency and throughput matter
Cost → intelligence needs economic discipline
Maintainability → another engineer should understand it
Product Impact → technology must solve a real problem
| Area | What I Engineer |
|---|---|
| 🤖 AI Agents | Multi-agent workflows, tool calling, orchestration, memory & state |
| 🧠 LLM Systems | Production LLM applications, structured outputs, evaluations |
| 📚 RAG | Enterprise knowledge systems, retrieval pipelines, vector search |
| ⚡ Backend | High-performance APIs, async systems, distributed services |
| ☁️ Cloud Native | Docker, Kubernetes, deployment & scalable infrastructure |
| 🔌 AI Infrastructure | MCP servers, AI gateways, observability & platform tooling |
| 🚀 Microservices | Go/Python services, event-driven architectures & integrations |
LLM Applications
├── Agentic AI
├── Multi-Agent Systems
├── RAG
├── Tool Calling
├── Structured Outputs
├── AI Workflows
├── MCP
├── Prompt Engineering
├── Evaluation
└── AI Observability
I’m particularly interested in architectures where an LLM is one component of a larger engineered system, not the entire application.
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│ Client / │
│ Product │
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│ API Gateway │
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│ AI Orchestrator │
│ LangGraph / etc. │
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┌────────────┼────────────┐
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┌─────────┐ ┌─────────┐ ┌─────────┐
│ Agent A │ │ Agent B │ │ Agent C │
└────┬────┘ └────┬────┘ └────┬────┘
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│ Tools / MCP / │
│ External APIs │
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Vector DB PostgreSQL Redis
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Enterprise Data
The interesting engineering isn't simply “call an LLM.”
It's making the entire system observable, controllable, secure and production-ready.
FastAPI · LangGraph · LLMs · Redis · PostgreSQL
A platform for orchestrating specialized AI agents, tool execution, state management and complex workflows.
Focus: orchestration · reliability · extensibility · production APIs
LLMs · RAG · Qdrant · PostgreSQL · FastAPI
Knowledge systems designed around retrieval quality, contextual answers and enterprise data boundaries.
Focus: retrieval · grounding · document pipelines · evaluation
FastAPI · Docker · Kubernetes · PostgreSQL · Redis
A production-oriented backend foundation for rapidly launching scalable APIs.
Focus: clean architecture · async workloads · containers · deployment
Go · PostgreSQL · Redis · REST APIs
High-performance backend services designed around simplicity, concurrency and operational reliability.
Focus: performance · concurrency · distributed systems
┌──────────────────────────────────────────────┐
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│ 🤖 Agentic AI │
│ 🧩 Multi-Agent Architecture │
│ 🔌 Model Context Protocol (MCP) │
│ 🧠 LLM Evaluation & Observability │
│ 📚 Enterprise RAG │
│ ⚡ High-Performance Go Services │
│ ☁️ Cloud-Native AI Platforms │
│ 🏗️ AI Infrastructure │
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If you're building something ambitious, I can contribute across the stack.
Need to turn an AI idea into a real product?
I can help architect the system from the first API to production infrastructure.
Need someone who can bridge AI + backend + infrastructure?
That's where I operate best.
Building agents, RAG, AI automation or LLM infrastructure?
Let's think beyond the prototype and design for production.
Have an interesting AI infrastructure problem?
I'm interested in building useful things with strong engineering fundamentals.
I'm especially interested in collaborating on:
AI Infrastructure
Agentic AI
Developer Tools
Enterprise AI
AI Automation
Distributed Systems
Cloud-Native Platforms
Open Source
If you're building something where AI meets serious engineering, I'd love to hear about it.
